import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

sess = tf.InteractiveSession()

def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)
            
def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

with tf.name_scope('define_input'):
    x = tf.placeholder(tf.float32, [None, 784], name='image_input')
x_image = tf.reshape(x, [-1,28,28,1])

W_conv1 = weight_variable([3, 3, 1, 32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

W_conv2 = weight_variable([3, 3, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

W_conv3 = weight_variable([3, 3, 64, 128])
b_conv3 = bias_variable([128])
h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3) + b_conv3)

W_conv4 = weight_variable([3, 3, 128, 10])
b_conv4 = bias_variable([10])
h_conv4 = tf.nn.relu(conv2d(h_conv3, W_conv4) + b_conv4)

with tf.name_scope('define_input'):
    keep_prob = tf.placeholder(tf.float32, name = 'keep_prob')
#h_conv4_drop = tf.nn.dropout(h_conv4, keep_prob)

net_output = tf.nn.avg_pool(h_conv4, ksize=[1, 7, 7, 1], strides=[1, 1, 1, 1], padding='VALID')
net_output = tf.reshape(net_output, [-1, 1*1*10])

with tf.variable_scope('output_labels'):
    y_conv=tf.nn.softmax(net_output)

y_ = tf.placeholder(tf.float32, [None, 10])

cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
sess.run(tf.initialize_all_variables())

for i in range(2000):
    batch = mnist.train.next_batch(50)
    if i%100 == 0:
        train_accuracy = accuracy.eval(feed_dict={x:batch[0], y_: batch[1], keep_prob: 1.0})
        print("step %d, training accuracy %g"%(i, train_accuracy))
    train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

print("test accuracy %g" % accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

graph = tf.get_default_graph()
input_graph_def = graph.as_graph_def()
output_graph_def = tf.graph_util.convert_variables_to_constants(
    sess=sess,
    input_graph_def=input_graph_def,
    output_node_names=['output_labels/Softmax'])
 
with tf.gfile.GFile('mnist_deep.pb', "wb") as f:
    f.write(output_graph_def.SerializeToString())
